Rapid improvements in emotion regulation predict eating disorder psychopathology and functional impairment at 6‐month follow‐up in individuals with bulimia nervosa and purging disorder
Bibliographic record
Abstract
OBJECTIVE: We previously demonstrated that early improvements in access to emotion regulation strategies during the first 4 weeks of intensive cognitive behavior therapy (CBT)-based eating disorder (ED) treatment predicted a range of post-treatment outcomes. This follow-up article examines whether early improvements in access to emotion regulation strategies continue to predict good treatment outcomes at 6 months post-treatment. METHOD: Participants were 76 patients with bulimia nervosa or purging disorder who participated in the original study and the 6-month follow-up assessment. Hierarchical regression models were used to examine whether early improvements in emotion regulation strategies predicted 6-month follow-up outcomes. RESULTS: After controlling relevant covariates and rapid and substantial behavior change, greater early improvements in access to emotion regulation strategies during the first 4 weeks of intensive treatment predicted lower overall ED psychopathology and ED-related functional impairment 6 months after treatment. They did not predict abstinence from binge, vomit, and laxative use behaviors during the follow-up period. DISCUSSION: Individuals who learn early in treatment that they can use skills to more effectively regulate emotions have better treatment outcomes on some variables 6 months after treatment. Teaching emotion regulation skills in the first phase of CBT for ED may be beneficial, particularly for individuals with baseline difficulties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".